A dust removal vehicle path planning method based on multi-objective optimization

Abstract

The present application relates to the technical field of plant ash conveying scheduling, and particularly relates to a dust removal ash vehicle path planning method based on multi-objective optimization. The present application dynamically quantifies and evaluates the dust removal demand of the centralized ash bin by collecting the material level of each centralized ash bin in real time and calculating the dust removal urgency, so as to realize accurate judgment and active scheduling of the dust removal task, and significantly improve the timeliness of the dust removal response. Meanwhile, the present application screens out the target scheduling vehicle with the highest comprehensive scheduling priority by comprehensively considering the task readiness and spatial proximity of the candidate vehicle, realizes efficient collaborative scheduling of the dust removal vehicle, ensures timely response to the dust removal task, and greatly improves the utilization rate of the dust removal vehicle and the scientificity and orderliness of the scheduling.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of plant ash scheduling, in particular to a dust removal ash vehicle path planning method based on multi-objective optimization. BACKGROUND

[0002] In large industrial plants such as steel, power, metallurgy, etc., timely removal and transportation of dust removal ash is an important link to ensure production continuity and environmental compliance. The commonly used dust removal technology is mainly based on bag-type dust removal technology. The dust removal ash collected by the dust collector is sent to the centralized ash bin by mechanical ash conveying or pneumatic ash conveying, and then transported to different processing units according to the properties of the dust removal ash by the ash conveying vehicle.

[0003] Due to the different sizes of steel enterprises, there are a large number of centralized ash bins in them, and the volumes of each centralized ash bin are different. The dust removal ash collected by a single or multiple dust collectors is transported to a single or multiple centralized ash bins for temporary storage. Because the volume of the centralized ash bin is not completely the same as the volume of the ash conveying vehicle, the ash conveying vehicle will discharge ash to multiple regional centralized ash bins at a time.

[0004] However, the ash conveying process currently does not make systematic planning of the ash conveying route, relies on manual experience or fixed plans, is prone to subjective bias, and the stability and optimality of the transportation are difficult to guarantee, and it is easy to cause waste of personnel and resources. At the same time, there is a lack of real-time monitoring of the ash conveying execution process and a dynamic adjustment mechanism based on feedback, and the emergency ash conveying capability is weak. When the centralized ash bin stock level changes or the ash conveying vehicle itself has problems, it will seriously affect the normal operation of the ash conveying work and reduce the overall efficiency of the ash conveying process. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art, by fusing the ash conveying urgency of the centralized ash bin and the real-time transportation load of the plant digital road network, constructing a dynamic optimization region and scientifically scheduling the vehicle task readiness and spatial proximity, realizing intelligent, efficient and adaptive planning of the ash conveying path, and improving the overall efficiency and emergency response capability of the ash conveying operation.

[0006] The technical solution adopted by the present application to solve its technical problems is: a dust removal ash vehicle path planning method based on multi-objective optimization, comprising the following steps: real-time collection of the average traffic speed of each intersection of the plant road network and the stock level of each centralized ash bin; calculating the ash level change amount according to the stock level, and then analyzing to obtain the ash conveying urgency.

[0007] Divide the plant roads into multiple analysis sections, calculate the real-time transportation load according to the ash conveying urgency of the reachable centralized ash bin of each analysis section and the average traffic speed of the connected intersection.

[0008] According to the real-time transportation load, a core road section and its associated road section are identified, thereby forming an initial path optimization area, and then the initial path optimization area is expanded into a dynamic optimization area according to a preset rule.

[0009] The rated carrying capacity, real-time load and real-time position of all ash conveying vehicles are obtained, and available vehicles are screened out, and a target scheduling vehicle is screened out from the available vehicles.

[0010] A plurality of candidate paths from the real-time position of the target scheduling vehicle to each target centralized ash bin are planned; and the comprehensive path cost is calculated according to the total length of each candidate path and the corresponding real-time transportation load.

[0011] The path with the lowest comprehensive path cost is taken as the final ash conveying path and is sent to the execution terminal of the corresponding ash conveying vehicle.

[0012] Compared with the prior art, the present application has the following beneficial effects:

[0013] (1) The present application dynamically quantifies and evaluates the centralized ash bin ash conveying demand by collecting the material level height of each centralized ash bin in real time and calculating the ash conveying urgency, thereby realizing accurate judgment and active scheduling of the ash conveying task, and significantly improving the timeliness of the ash conveying response.

[0014] (2) The present application can accurately identify key congested road sections by dividing the plant road into analysis road sections and calculating the real-time transportation load; by constructing a dynamic optimization area, the path planning is focused on the core problem area, thereby planning an optimized path with short distance and low congestion risk, and improving the traffic efficiency of the plant road network and the reliability of the ash conveying path.

[0015] (3) The present application selects the target scheduling vehicle with the highest comprehensive scheduling priority by comprehensively considering the task readiness and spatial proximity of the candidate vehicles, realizes efficient collaborative scheduling of the ash conveying vehicles, ensures timely response to the ash conveying task, and greatly improves the utilization rate of the ash conveying vehicles and the scientificity and orderliness of the scheduling. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0017] Figure 1 The flowchart of the planning method of the present application.

[0018] Figure 2 The flowchart of the present application for dividing the plant road into a plurality of analysis road sections.

[0019] Figure 3A flow chart for extending the initial path optimization area to a dynamic optimization area according to a preset rule for the present application. DETAILED DESCRIPTION

[0020] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. Note that the relative arrangement, numerical expressions, and numerical values of components and steps set forth in these embodiments are not limiting to the scope of the present application unless specifically stated otherwise. Also, it should be understood that the dimensions of the various portions shown in the drawings are not drawn to scale for ease of description.

[0021] The following description of at least one example embodiment is merely illustrative in nature and is in no way limiting to the scope of the application and its applications or uses. Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered within the scope of the disclosure where appropriate.

[0022] In all examples shown and discussed herein, any specific values should be interpreted as merely illustrative and not as a limitation. Thus, other examples of the example embodiments can have different values.

[0023] Referring to Figure 1 As shown in the figure, a dust ash vehicle path planning method based on multi-objective optimization includes the following steps: S1, collecting the average passing speed of each intersection of the plant road network and the material level of each centralized ash bin in real time; calculating the ash level change quantity according to the material level, and then analyzing to obtain the ash conveying urgency.

[0024] In the present application, the average passing speed can be obtained by installing a traffic flow monitoring sensor at each intersection in the plant area, collecting the time and distance of the ash conveying vehicle passing through the intersection in real time, and calculating according to the time and distance.

[0025] The specific implementation process of the above scheme is: calculating the difference value of the material level of each centralized ash bin at adjacent collection time, and dividing the difference value by the time interval at adjacent collection time to obtain the ash level change quantity.

[0026] The ratio of the ash level change quantity to the rated capacity of the corresponding centralized ash bin is taken as the ash level change rate; the ratio of the current material level of each centralized ash bin to its rated material level is calculated, and the ratio is weighted and fused with the corresponding ash level change rate to obtain the ash conveying urgency.

[0027] In the present application, the ash level change rate is used to predict the future state of the ash bin. The greater the value, the faster the centralized ash bin is filling, the higher the ash conveying urgency, and the ash conveying vehicle needs to be dispatched in time to avoid overflow.

[0028] The ratio of the current material level height to the rated material level height represents the current filling degree of the centralized ash bin. The larger the ratio, the closer the centralized ash bin is to full load, and the more urgent the ash conveying demand.

[0029] The present application considers both the current filling degree and the future trend, avoiding the risk of ignoring rapid changes by only considering the current filling degree, and the problem of ignoring ash bins that are close to full load by only considering the change rate.

[0030] It should be noted that the rated capacity refers to the maximum amount of dust that the centralized ash bin can safely store in design, which can be calculated by the volume geometry formula, for example, a cylindrical centralized ash bin: wherein, is the rated capacity, is the inner diameter, is the circumference, is the effective height, usually 10%-20% of the total height of the centralized ash bin, and the rated material level height refers to the effective height corresponding to the rated capacity of the centralized ash bin.

[0031] In addition, when linearly weighting and summing the ratio and the corresponding ash level change rate, the corresponding weights of the two can be 0.7 and 0.3, respectively. Of course, the weight values can also be adjusted according to the season, for example, in the rainy season or high humidity season, dust is easy to be hardened and stick to the bin in the humid environment, at this time, the weight corresponding to the ash level change rate can be increased to 0.6, and the weight corresponding to the ratio can be 0.4.

[0032] S2, divide the plant road into multiple analysis road sections, calculate the real-time transportation load according to the ash conveying urgency of the reachable centralized ash bin of each analysis road section and the average traffic speed of the connected intersection.

[0033] Among them, S21, the plant road is divided into multiple analysis road sections, including: based on the plant digital road network, all road intersections are taken as nodes, and the road is divided into initial road sections connecting adjacent intersections. Ensure that each initial road section has a road intersection at both ends, and there is no other intersection interference in the initial road section.

[0034] Identify the entrances and exits of the centralized ash bin directly connected to each initial road section; for any initial road section: if its corresponding entrance and exit are unique or do not exist, the initial road section is taken as an analysis road section; otherwise, the initial road section is divided into multiple sub-road sections according to the position of the entrance and exit, and each sub-road section is taken as an analysis road section.

[0035] The corresponding entrance and exit are unique or do not exist, which means that an initial road section is directly connected to only one entrance and exit of an ash bin, or it is not connected to any ash bin at all. At this time, the traffic speed and ash conveying urgency of the entire initial road section are homogeneous, and the initial road section is taken as an analysis road section as a whole, which can accurately reflect its function and avoid introducing errors.

[0036] When an initial road segment directly connects two or more entrances, through refined road segment division, the system can identify the real congestion bottleneck section in the factory area digital road network, dynamically focus on the key area, and facilitate the subsequent generation of effective dynamic optimization area.

[0037] S22, the real-time transportation load is calculated according to the reachable ash storage urgency of each analysis road segment and the average passing speed of the intersection connected by the analysis road segment, and specifically, the maximum value of the reachable ash storage urgency of all analysis road segments is selected as the demand urgency.

[0038] If the average value is selected, when one centralized ash storage urgency is extremely high and one centralized ash storage urgency is very low, the demand urgency of the analysis road segment may be pulled to a medium level, which cannot accurately reflect the key road segment, so that the system cannot identify the key road segment that has the greatest impact on the global ash transportation task, thereby delaying the most important ash transportation task.

[0039] The ratio of the real-time average passing speed of the intersection connected by each analysis road segment to the average passing speed of all intersections of the factory area road network in the same period is calculated to obtain the passing efficiency coefficient.

[0040] When the passing efficiency coefficient is less than 1, it means that the passing efficiency of the analysis road segment is lower than the global level, indicating that the analysis road segment is congested; when the passing efficiency coefficient is greater than or equal to 1, it means that the passing efficiency of the analysis road segment is higher than the global level, indicating that the analysis road segment is relatively smooth.

[0041] The product of the demand urgency and the passing efficiency coefficient is taken as the real-time transportation load of each analysis road segment.

[0042] Multiplying the demand urgency and the passing efficiency coefficient can accurately screen out those analysis road segments that are both critical points and traffic bottlenecks.

[0043] For example, analysis road segment A: demand urgency is 0.9, passing efficiency coefficient is 0.5; analysis road segment B: demand urgency is 0.9, passing efficiency coefficient is 1.2.

[0044] Although analysis road segment A and analysis road segment B both have high demand, analysis road segment A has a real transportation load that reflects its huge pressure as a bottleneck because of congestion. Although analysis road segment B has high demand, its transportation load more reflects its high value rather than high risk because of its strong passing capacity. At this time, the system will preferentially screen out analysis road segment A.

[0045] S3, according to the real-time transportation load, the core road segment and its associated road segment are identified, thereby forming an initial path optimization area, which is then expanded into a dynamic optimization area according to the preset rules.

[0046] The initial path optimization area accurately frames the current core problem point. However, when the system starts scheduling the ash conveying vehicle according to the initial area, the change of the ash conveying vehicle flow may immediately affect the surrounding road sections adjacent to the initial path optimization area, causing new congestion in the initial path optimization area.

[0047] Therefore, the present application realizes the active management and prediction of traffic flow by dynamic expansion based on the initial path optimization area, effectively preventing congestion from spreading and diffusing.

[0048] In the step S31, the core road section and its associated road section are identified according to the real-time transportation load, and the initial path optimization area is formed.

[0049] The analysis road sections are sequentially selected from the sequence start until the sum of the real-time transportation loads of all selected analysis road sections reaches the preset proportion of the total transportation load, and the selected analysis road sections are determined as the core road sections.

[0050] The number of analysis road sections and the total transportation load of different factory areas are quite different, and the total transportation load fluctuates. If a fixed number of road sections is used, it will lead to excessive coverage of core road sections in small factories and insufficient coverage in large factories. If a fixed load threshold is used, it may result in no core road section in a factory area with low total transportation load, and too many core road sections in a factory area with high total transportation load.

[0051] The present application adapts to the dynamics and differences of the factory road network through the cumulative proportion method, ensuring the scientificity and universality of core road section identification.

[0052] It should be noted that the preset proportion is usually 60%-80% to ensure that the core road section covers most of the transportation load and avoids including too many low-transportation-load analysis road sections, leading to redundancy in the initial area. In the present application, the preset proportion is preferably 70%.

[0053] All analysis road sections directly connected to each core road section are determined as the associated road section of the core road section, and all core road sections and their associated road sections are determined as the initial path optimization area.

[0054] Directly connected means that two analysis road sections share the same intersection, and no other analysis road section is connected to both at the node.

[0055] The ash conveying function of the core road section needs to be realized through the associated road section, and if only the core road section is included in the initial area, the optimization chain breakage problem of the isolated core road section and the ash conveying vehicle unable to arrive will occur, leading to path planning failure.

[0056] S32, the initial path optimization area is extended according to a preset rule to obtain a dynamic optimization area.

[0057] In the first time period, the real-time transportation load of each analysis road segment in the initial path optimization area is continuously monitored; when the average transportation load of the initial path optimization area in the first time period continuously exceeds the historical average transportation load, first-layer expansion is performed: all analysis road segments directly adjacent to the periphery of the initial path optimization area are included in the expansion range.

[0058] When the average transportation load continuously exceeds the historical average transportation load, it indicates that the analysis road segments in the initial path optimization area have approached or reached the upper limit of the traffic capacity, and the continued concentration of ash conveying vehicles in the initial path optimization area will cause congestion, so the range of the initial path optimization area needs to be expanded.

[0059] The analysis road segments adjacent to the periphery of the initial path optimization area are directly connected to the initial path optimization area through intersections, and the ash conveying vehicles can quickly switch from the initial path optimization area to these analysis road segments without detours, so the first-layer expansion can effectively avoid increasing the total path length and reducing the ash conveying efficiency.

[0060] In the second time period, the peripherally adjacent road segments of the region after the first-layer expansion are monitored; when the transportation load growth rate of any peripherally adjacent road segment in the second time period exceeds the historical average growth rate, second-layer expansion is performed: the peripherally adjacent road segment and all analysis road segments directly connected thereto are included in the expansion range.

[0061] After the first-layer expansion, the peripherally adjacent road segments become shunt channels of the initial path optimization area, and the transportation load growth rate thereof reflects the trend of shunt pressure change. When the transportation load growth rate of any peripherally adjacent road segment exceeds the historical average growth rate, it indicates that the shunt pressure thereof is rapidly rising and may reach the upper limit of the traffic capacity in the short term, so further expansion is needed.

[0062] The transportation load growth of the peripherally adjacent road segments is usually transmitted to the analysis road segments directly connected thereto, so by performing the second-layer expansion, chain diffusion of congestion can be avoided, and the expanded region can continue to bear the shunt function.

[0063] The analysis road segments included in the initial path optimization area, the first-layer expansion, and the second-layer expansion are taken as the final dynamic optimization area.

[0064] It should be noted that if the transportation load of the initial path optimization area of the factory area fluctuates little and the congestion of the peripherally adjacent analysis road segments spreads quickly, the first time period can be 6 minutes and the second time period can be 3 minutes.

[0065] If the transportation load in the initial route optimization area of ​​the factory area fluctuates greatly and the growth rate of the adjacent analysis road segments fluctuates greatly, then the first time period can be 8 minutes and the second time period can be 12 minutes.

[0066] S4. Obtain the rated capacity, real-time load and real-time location of all ash transport vehicles, filter out available vehicles and select target dispatch vehicles from them. The specific process is as follows: calculate the difference between the rated capacity and real-time load of each ash transport vehicle. If the difference is greater than zero, it is determined that the ash transport vehicle has remaining capacity and is selected as a candidate vehicle.

[0067] Rated capacity is the maximum ash carrying capacity designed for ash transport vehicles. A difference greater than zero between rated capacity and real-time load directly represents the additional transport capacity that the ash transport vehicle can add, which is a prerequisite for undertaking new ash transport tasks. If the ash transport vehicle has no additional transport capacity, it cannot complete the ash transport task even with scheduling, and is considered an invalid candidate. Excluding such ash transport vehicles can avoid scheduling failures and delays in transporting ash from high-urgency centralized ash silos.

[0068] The task status records of all candidate vehicles in the plant's ash conveying task scheduling system are retrieved to determine the estimated remaining task time for each candidate vehicle, and then the task readiness is analyzed.

[0069] Calculate the shortest travel distance from the real-time location of each candidate vehicle to the boundary of the dynamically optimized area based on the factory's digital road network, and use this distance as the spatial proximity.

[0070] The factory road network has physical constraints such as road intersections, dedicated lanes, and no-entry zones. Calculating the straight-line distance from the candidate vehicle's location to the dynamic optimization area will not reflect the actual driving path.

[0071] The shorter the distance the candidate vehicle travels, the less time it takes for the candidate vehicle to travel empty to the target area, and the faster it can start performing the ash transport task.

[0072] It should also be noted that the boundary of the dynamic optimization area is the line connecting the outermost nodes of all analyzed road segments within the dynamic optimization area. If the analyzed road segment is a straight line, the boundary is the circumscribed polygon of the endpoint of the analyzed road segment; if there is a closed loop formed by the analyzed road segments, the boundary is the outer contour line of the area enclosed by the closed loop formed by the analyzed road segments, to ensure the accuracy of spatial proximity calculation.

[0073] The task readiness and spatial proximity are weighted and fused to obtain the comprehensive scheduling priority of each candidate vehicle.

[0074] Task readiness reflects the temporal availability of candidate vehicles, i.e., how long it takes for a candidate vehicle to start a new ash conveying task. Spatial proximity reflects the spatial accessibility of candidate vehicles, i.e., the distance cost from the candidate vehicle to the task area.

[0075] By comprehensively considering the task readiness and spatial proximity, the balance of time and space dimensions is achieved, and the scheduling inefficiency caused by considering only a single dimension is avoided.

[0076] For example, only considering the task readiness, a candidate vehicle that can quickly start but has a long travel path may be scheduled, resulting in delayed ash delivery; only considering the spatial proximity, a candidate vehicle that is close in distance but needs to wait for a long time may also delay ash delivery.

[0077] It should be noted that when linearly weighting and summing the task readiness and spatial proximity, in the present application, the weights of the two can be exemplarily taken as 0.6 and 0.4, respectively. In addition, in an emergency situation where every second counts, the weight of spatial proximity can be increased; when the task completion rate and order of the candidate vehicle are emphasized, the weight of task readiness can be increased.

[0078] According to the comprehensive scheduling priority, all candidate vehicles are sorted in descending order, and the vehicle or vehicles ranked first are determined as the target scheduling vehicle.

[0079] The larger the comprehensive scheduling priority value is, the better the comprehensive performance of the candidate vehicle in time and space dimensions is, which meets the needs of timely and efficient execution of the ash delivery task; by descending arrangement, the system can quickly and preferentially select the candidate vehicle, maximize the benefit of each scheduling decision, and ensure the running efficiency of the plant ash delivery scheduling system.

[0080] When there is only one centralized ash bin in the dynamic optimization area that urgently needs to be delivered, and the carrying capacity of a candidate vehicle is sufficient, one candidate vehicle with the highest priority can be selected.

[0081] When multiple centralized ash bins in the dynamic optimization area simultaneously urgently need to be delivered, or the demand of one centralized ash bin is large, multiple candidate vehicles ranked first can be selected to jointly complete the ash delivery task.

[0082] In S41, the task readiness of each candidate vehicle is determined, and the task readiness is analyzed as follows: for a candidate vehicle without assigned tasks, the predicted remaining task time is zero, and the task readiness is a preset highest priority value.

[0083] The preset highest priority value is usually the normalized upper limit of the task readiness index of the system, i.e., 1.0. This ensures that the vehicle without assigned tasks has the highest priority in the task readiness dimension.

[0084] For a candidate vehicle with assigned tasks in the execution state, the predicted remaining task time is the ratio of the current remaining carrying capacity to the average loading and unloading efficiency, and the task readiness is the reciprocal of the predicted remaining task time.

[0085] The task readiness degree quantifies the urgency of the candidate vehicle from the current state to the new task, the shorter the estimated remaining task time, the earlier the candidate vehicle can release and enter a new task, and the task readiness degree should be higher, so the inverse of the estimated remaining task time is taken as the task readiness degree.

[0086] It should be noted that the average loading and unloading efficiency is obtained by historical loading and unloading data statistics, specifically: collecting the loading and unloading weight and time data of the candidate vehicle / same type of ash conveying vehicle in the past 30 days, calculating the average value of the loading and unloading weight per unit time, and obtaining the average loading and unloading efficiency.

[0087] For the candidate vehicle to which the task to be allocated has been pre-allocated, the estimated remaining task time is the difference between the planned start time of the pre-allocated task and the current time, and the task readiness degree is the inverse of the difference.

[0088] The candidate vehicle has pre-allocated tasks, and the task has not started, so it cannot take a new task before the planned start time of the pre-allocated task, so the difference between the planned start time and the current time directly reflects how long the candidate vehicle needs to wait to start the pre-allocated task, and it cannot take a new task during the waiting period, that is, the shortest waiting time for the candidate vehicle to take a new task.

[0089] The shorter the waiting time for the candidate vehicle to release and take a new task, the earlier it can enter the pre-allocated task execution process, and the earlier it can take a new task after execution. Therefore, the inverse of the difference between the planned start time and the current time is taken as the task readiness degree.

[0090] S5, planning multiple candidate paths for the target scheduling vehicle from its real-time location to each target centralized ash bin; calculating the comprehensive path cost according to the total length of each candidate path and the corresponding real-time transportation load.

[0091] Among them, S51, the multiple candidate paths for the target scheduling vehicle from its real-time location to each target centralized ash bin are specifically: based on the factory digital road network to construct a topological network, taking the analysis section as the edge and the road intersection as the node.

[0092] Taking the real-time location of each target scheduling vehicle as the path starting point; taking the location of each centralized ash bin in the dynamic optimization area whose ash conveying urgency reaches the preset level as the path target point.

[0093] The target scheduling vehicle needs to go to the centralized ash bin to execute the ash conveying task, and the physical starting point of the ash conveying task is necessarily the current location. Taking its real-time location as the path starting point can ensure the timeliness of path planning and avoid path failure due to lagging starting point.

[0094] The path target point is limited in the dynamic optimization area, so that the planned path mainly uses the optimized core road section, and the path avoids involving non-optimized road sections outside the dynamic optimization area. The positions of the concentrated ash bins with the ash delivery urgency reaching the preset level are taken as the path target points, so as to avoid the target scheduling vehicle preferentially processing non-urgent tasks, and the high-urgency ash bin facing the risk of full bin due to no scheduling.

[0095] The preset level mentioned above can be determined according to historical data. In the present application, the preset level can be 0.7 for example.

[0096] In the topological network, a multi-path search algorithm is used to generate multiple shortest paths for each combination of a starting point and a target point.

[0097] In the present application, the multi-path search algorithm can be a K-shortest path algorithm. Multiple shortest paths are generated simultaneously to ensure that when one path fails, the other path can be quickly switched to, so as to avoid delay of the ash delivery task.

[0098] Each analysis road section passed through by each shortest path is traversed. If all the analysis road sections are located in the dynamic optimization area and are marked as a connected state in the current plant digital road network, the shortest path is taken as a candidate path, otherwise the shortest path is excluded.

[0099] The connected state is a basic prerequisite for the path to be executable. If an analysis road section is marked as a disconnected state, for example, due to construction closure, equipment failure or the like, the path containing the analysis road section cannot be actually driven, and must be excluded, otherwise the planned path has no execution significance.

[0100] S52, the comprehensive path cost is calculated according to the total length of each candidate path and the corresponding real-time transportation load. Specifically, for each target scheduling vehicle and all candidate paths generated by the same path starting point and path target point corresponding to the target scheduling vehicle, the following calculation is performed: the ratio of the total length of each candidate path to the maximum value in the total length of all candidate paths is taken as a length cost coefficient.

[0101] The arithmetic mean of the real-time transportation loads of all analysis road sections passed through by each candidate path is taken as a path average load.

[0102] The target scheduling vehicle needs to drive all analysis road sections passed through by the candidate path, so the overall traffic pressure of the candidate path is jointly determined by the transportation loads of all analysis road sections.

[0103] The ratio of each path average load to the maximum value in the path average loads is taken as a load cost coefficient, and the product of the length cost coefficient and the load cost coefficient is taken as the comprehensive path cost.

[0104] The length cost coefficient controls the time risk, and if the length cost coefficient is ignored, the system may select a candidate path with low transportation load but extremely long length, resulting in too long transportation time.

[0105] The load cost coefficient controls the traffic risk, and if the load cost coefficient is ignored, the system may select a candidate path with short length but extremely high transportation load, resulting in too long queuing time of the target scheduling vehicle on the candidate path, which also leads to delay in transportation of ash and cannot realize efficient transportation of ash.

[0106] The present application considers the length cost coefficient and the load cost coefficient comprehensively, ensures the accuracy of optimal candidate path selection, adapts to the scheduling demand of dynamic optimization area, and guarantees the core target of simultaneously optimizing the efficiency of transportation of ash and the smoothness of road network traffic.

[0107] S6. The path with the lowest comprehensive path cost is taken as the final transportation path of ash and is issued to the execution terminal of the corresponding vehicle for transportation of ash.

[0108] The execution terminal mentioned above can be a vehicle-mounted device with wireless communication and navigation functions, which can receive the final transportation path of ash issued by the factory area transportation task scheduling system and provide navigation guidance for the driver to execute the transportation task of ash.

[0109] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized in the form of a computer program product wholly or partially.

[0110] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0111] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0112] The above is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0113] ​​​​​​​​​Finally, the above only is the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for dusting ash vehicle path planning based on multi-objective optimization, characterized in that, The method comprises the following steps: Real-time collection of average passing speed of each intersection of the plant road network and material level height of each centralized ash bin; Calculation of ash level change amount according to the material level height, and further analysis of the ash conveying urgency; Division of the plant road into multiple analysis road segments, calculation of real-time transportation load according to the ash conveying urgency of the ash bins reachable by each analysis road segment and the average passing speed of the intersections connected by the analysis road segment; Identification of core road segments and associated road segments according to the real-time transportation load, thereby forming an initial path optimization area, and expansion of the initial path optimization area into a dynamic optimization area according to a preset rule; Obtaining of the rated carrying capacity, real-time load and real-time position of all ash conveying vehicles, screening of available vehicles and further screening of target scheduling vehicles from the available vehicles; Planning of multiple candidate paths from the real-time position of the target scheduling vehicle to each target centralized ash bin, calculation of comprehensive path cost according to the total length of each candidate path and the corresponding real-time transportation load; Downlinking of the path with the lowest comprehensive path cost to the execution terminal of the corresponding ash conveying vehicle as the final ash conveying path; Calculation of the ash level change amount according to the material level height, and further analysis of the ash conveying urgency, specifically: calculation of the difference between the material level heights of each centralized ash bin at adjacent collection time points, and division of the difference by the time interval between the adjacent collection time points to obtain the ash level change amount; taking the ratio of the ash level change amount to the rated capacity of the corresponding centralized ash bin as the ash level change rate; calculating the ratio of the current material level height of each centralized ash bin to the rated material level height, and performing weighted fusion of the ratio and the corresponding ash level change rate to obtain the ash conveying urgency; Calculation of the real-time transportation load according to the ash conveying urgency of the ash bins reachable by each analysis road segment and the average passing speed of the intersections connected by the analysis road segment, specifically: selecting the maximum value of the ash conveying urgencies of all reachable centralized ash bins of each analysis road segment as the demand urgency; calculating the ratio of the real-time average passing speed of the intersections connected by each analysis road segment to the average passing speed of all intersections of the plant road network at the same time period to obtain the passing efficiency coefficient; Taking the product of the demand urgency and the passing efficiency coefficient as the real-time transportation load of each analysis road segment; Expansion of the initial path optimization area into a dynamic optimization area according to a preset rule, specifically: setting a continuous first time period and a second time period; in the first time period, continuously monitoring the real-time transportation load of each analysis road segment in the initial path optimization area; when the average transportation load of the initial path optimization area in the first time period continuously exceeds the historical average transportation load, performing a first layer expansion: including all analysis road segments directly adjacent to the periphery of the initial path optimization area in the expansion range; in the second time period, monitoring the peripherally adjacent road segments of the area after the first layer expansion; When the transportation load growth rate of any peripherally adjacent road segment in the second time period exceeds the historical average growth rate, performing a second layer expansion: including the peripherally adjacent road segment and all analysis road segments directly connected thereto in the expansion range; taking the analysis road segments included in the initial path optimization area, the first layer expansion and the second layer expansion as the final dynamic optimization area; The comprehensive path cost is calculated according to the total length of each candidate path and the corresponding real-time transportation load, specifically: for each target scheduling vehicle and all candidate paths generated by the same path starting point and path target point corresponding to the target scheduling vehicle, the following calculation is performed: the ratio of the total length of each candidate path to the maximum value in the total length of all candidate paths is calculated as a length cost coefficient; The arithmetic mean of the real-time transportation load of all analysis road segments passed by each candidate path is calculated as the path average load; the ratio of each path average load to the maximum value in all path average loads is calculated as a load cost coefficient; The product of the length cost coefficient and the load cost coefficient is taken as the comprehensive path cost.

2. The multi-objective optimization based dusting ash vehicle routing method according to claim 1, wherein, The factory area road is divided into multiple analysis road segments, including: Based on the factory area digital road network, all road intersections are taken as nodes, and the roads are divided into initial road segments connecting adjacent intersections; The entrances and exits of the centralized ash bunker directly connected to each initial road segment are identified; For any initial road segment: if its corresponding entrance and exit are unique or do not exist, the initial road segment is taken as an analysis road segment; otherwise, the initial road segment is divided into multiple sub-road segments according to the positions of the entrances and exits, and each sub-road segment is taken as an analysis road segment.

3. The multi-objective optimization based dusting ash vehicle routing method according to claim 1, wherein, According to the real-time transportation load, the core road segment and its associated road segment are identified to form an initial path optimization area, specifically: The analysis road segments are sorted in descending order according to the real-time transportation load; From the beginning of the sequence, the analysis road segments are selected one by one until the sum of the real-time transportation loads of all selected analysis road segments reaches a preset proportion of the total transportation load, and the selected analysis road segments are determined as core road segments; All analysis road segments directly connected to each core road segment are determined as the associated road segment of the core road segment; All core road segments and their associated road segments are taken as the initial path optimization area.

4. The multi-objective optimization based dusting ash vehicle routing method according to claim 1, wherein, The available vehicles are screened out, and the target scheduling vehicle is screened out from among them, specifically: The difference between the rated carrying capacity and the real-time load of each ash conveying vehicle is calculated, and if the difference is greater than zero, the ash conveying vehicle is determined to have residual carrying capacity and is taken as a candidate vehicle; The task state records of all candidate vehicles in the factory area ash conveying task scheduling system are retrieved to determine the expected residual task time of each candidate vehicle, and then the task readiness is analyzed; The shortest driving distance based on the factory area digital road network from the real-time position of each candidate vehicle to the boundary of the dynamic optimization area is calculated as the spatial proximity; The task readiness and the spatial proximity are weighted and fused to obtain the comprehensive scheduling priority of each candidate vehicle; All candidate vehicles are sorted in descending order according to the comprehensive scheduling priority, and the vehicle or vehicles ranked first are determined as the target scheduling vehicle.

5. The multi-objective optimization based dusting ash vehicle routing method according to claim 4, wherein, The expected residual task time of each candidate vehicle is determined to analyze the task readiness, specifically: For a candidate vehicle that has not been assigned a task, the expected residual task time is zero, and the task readiness is a preset highest priority value; For a candidate vehicle that has been assigned a task and is in execution, the expected residual task time is the ratio of the current residual carrying capacity to the average loading and unloading efficiency, and the task readiness is the inverse of the expected residual task time. For the candidate vehicle to which the task to be allocated has been pre-allocated, the predicted remaining task time is the difference between the planned start time of the pre-allocated task and the current time, and the task readiness degree is the inverse of the difference.

6. The multi-objective optimization based dusting ash vehicle routing method according to claim 4, wherein, Specifically, the method comprises the following steps: Based on the factory digital road network, a topological network is constructed, and the analysis road sections are taken as edges and the road intersections are taken as nodes. The real-time position of each target scheduling vehicle is taken as a path starting point, and the positions of each centralized ash bin in the dynamic optimization region whose ash delivery urgency reaches a preset level are taken as path target points. In the topological network, a multi-path search algorithm is used to generate multiple shortest paths for each combination of starting point and target point. Each analysis road section passed through by each shortest path is traversed, and if all the analysis road sections are located in the dynamic optimization region and are marked as a connected state in the current factory digital road network, the shortest path is taken as a candidate path.

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